CPSC 330 Lecture 23: Deployment and conclusion

Varada Kolhatkar

Focus on the breath!

Imagine you’ve created a machine learning model and are eager to share it with others. Consider the following scenarios for sharing your model:

To a non-technical Audience: How would you present your model to friends and family who may not have a technical background?
To a technical audience: How would you share your model with peers or professionals in the field who have a technical understanding of machine learning?
In an academic or research setting: How would you disseminate your model within academic or research communities?

After we train a model, we want to use it!
The user likely does not want to install your Python stack, train your model.
You don’t necessarily want to share the dataset.
So we need to do two things:
1. Save/store your model for later use.
2. Make the saved model conveniently accessible.

https://canvas.ubc.ca/courses/170662/external_tools/53187

They help us improve our teaching!
UBC & CS uses them to provide rewards to instructors and TAs who are doing well!
UBC & CS uses them to identify where instructors, TAs and courses need additional supports to improve.
UBC uses these in evaluating professors for tenure and promotion.
I’ll very much appreciate your constructive and concrete feedback.

Part 1: Supervised learning on tabular data: ML fundamentals, preprocessing and data encoding, a bunch of models, evaluation metrics, feature importances and model transparency, feature selection, hyperparameter optimization
Part 2: Dealing with other non-tabular data types: Clustering, recommender systems, computer vision with pre-trained deep learning models (high level), language data, text preprocessing, embeddings, topic modeling, time series, right-censored data / survival analysis
Part 3: Communication, Ethics, and Deployment

If you want to further develop your machine learning skills:

Practice!
Work on your own projects. Make your work available and reproducible.
If you are interested in research in machine learning
- Take CPSC 340. If you do not have the required prereqs you can try to audit it.
- Get into the habit of reading papers and replicating results

For each of the scenarios below identify whether ML is a good solution for the problem. If yes

Frame the task as a machine learning problem (classification, regression, clustering, forecasting, survival analysis, recommendation, etc.).
Identify key features you would need to solve the problem effectively.
Propose a reasonable baseline model or heuristic to compare against.
Choose an appropriate ML model for the scenario and explain why it’s suitable.
Specify success metrics that make sense for the task.

App	Goal
QueuePredictor app	Inform callers how long they will wait on hold given the current call volume.
To-doList App	Keep track of tasks a user enters and organize them by date.
SegmentSphere App	Segment customers to tailor marketing strategies based on purchasing behaviour.
Video app	Recommend useful or personalized videos to users.
Dining app	Identify the cuisine type from a restaurant’s menu.
Weather app	Estimate precipitation amounts in 6-hour increments for a geographic region.
EvoCarShare app	Predict the number of car rentals in 4-hour increments at a particular Evo parking spot.
Pharma app	Understand the effect of a new drug on patient survival time.

That’s all! We made it! I hope you learned something useful from the course. You all are wonderful students and I had fun teaching this course ♥️!

If you didn’t fill out course evaluations during class , it’ll be great if you can fill them in when you get a chance.